omnicart-pipeline 0.1.1

A simple API wrapper

Order Pipeline Project

Overview

The Order Pipeline Project is a Python-based data processing and analysis system designed to retrieve, enrich, analyze, and export product and user data. This project leverages API data, Pandas for data manipulation, and JSON for output storage. It is structured to simulate an end-to-end analytics pipeline.

The primary goal of this project is to demonstrate a clean, modular approach to data processing, suitable for testing and extension.

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Features

1. Data Retrieval
   Fetches products and users from APIs using APIClient. The APIClient implements a pagination logic that makes on request to the /products api and stores the data. This data is now used to simulate pagination using slicing vai the limits

2. Data Enrichment
   Converts API responses into Pandas DataFrames and merges them on user IDs to enrich product data with seller information, including username, email, and name. Calculates total revenue for each product.

3. Data Analysis
   Performs key metrics calculations such as total products per seller, total revenue, and average price per seller.

4. Exporting
   Exports analysis results into JSON files for easy storage and sharing.

5. Configuration Management
   Centralized configuration file handling using ConfigManager to manage project settings.

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Pipeline Workflow

The pipeline follows a modular, step-by-step process to fetch, enrich, analyze, and export data.

ConfigManager : Loads the configuration from the `popeline.cfg` file
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APIClient
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Fetch Products & Users. Uses the configuration settings loaded in be the configmanager
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DataEnricher
├─ Convert products to DataFrame
├─ Convert users to DataFrame
├─ Merge products & users on user ID
└─ Calculate revenue per product
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Analyzer
├─ Total products per seller
├─ Total revenue per seller
├─ Average price per seller
└─ Generate analysis dictionary
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Exporter
└─ Export analysis results to JSON

Step-by-Step Description:

• APIClient: Fetches product and user data from the API.

• DataEnricher: Converts raw JSON to DataFrames, merges datasets on user ID, and calculates revenue for each product.

• Analyzer: Computes key metrics for reporting.

• Exporter: Saves the results into a JSON file for further use.

Installation

1. Create a virtual environment and activate it

python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

2. Install the dependencies required

pip install pandas requests

4. Install the package

pip install <name of package>

Usage

In the activated environment and at the root folder, type in omnicart-pipeline:

omnicart-pipeline